This paper presents a machine learning–based approach for detecting and classifying faults in electrical power transmission lines. With the growing global demand for electricity, transmission systems face increasing stress, while their capacity expansion has not kept pace. As a result, effective fault detection techniques are essential for maintaining system reliability and stability. This study investigates the most common types of transmission line faults and utilizes various machine learning algorithms to classify them accurately. By employing different combinations of input features, the proposed method enhances the precision of fault identification. The machine learning models are developed and tested using the Spyder IDE (Scientific Python Development Environment). The results demonstrate that the adopted techniques effectively address the objectives of fault detection and classification in transmission networks.